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Scripps bioinformatics seminar_day_2


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Part 2 of introduction to knowledge representation and applications for knowledge discovery in bioinformatics

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Scripps bioinformatics seminar_day_2

  1. 1. Day 2 of Computing on the shoulders of giants: how existing knowledge is represented and applied in bioinformatics Benjamin Good Assistant Professor of the Department of Molecular and Experimental Medicine
  2. 2. Recap from Day 1 • Make things (articles, genes, antibodies, etc.) easier to find • Answer questions • Generate hypotheses Controlled vocabularies (MeSH) Ontologies (Gene Ontology) knowledge graphs on the Web: the SPARQL query language knowledge plus computation = inference, the ABC model
  3. 3. Computing with knowledge • Challenges with knowledge graphs • Too much data • ->> query, sort, visualize, interact • Not enough data • ->> mine for more.. • Goal for practical day: Go beyond PubMed! • gain hands on experience using a knowledge graph • either with tools built for the purpose or with your own code…
  4. 4. Assignment: knowledge graph to hypothesis • Option 1 Coding • Implement and apply an ABC Model style hypothesis generating program (can adapt from example provided) • explain its logic, explain how you used it to generate a hypothesis, explain the hypothesis (provide a visual) • Option 2 Non-coding • Use a knowledge discovery application(s) (list provided) to define a new hypothesis • if you can’t think of where to start, try to explain why Metformin may contribute to cancer survival • Assignment deliverables: a document containing • the inputs you gave to your program or the online tool(s) you used • what was generated in response and the underlying logic • an image and text describing the results, especially any hypothesis you could derive • (for Option 1 also submit any code written or files generated as a tar or zip archive)
  5. 5. Online tools for knowledge discovery • (* we make this one…) • (this is a good tool, but often breaks down) • (also on the flaky side, but can be good) • (works okay, requires a (free) account) • (ugly interface, but good tool)
  6. 6. Demos • • •
  7. 7. Example question: repurposing all drugs ?drug ?disease interacts with protein geneencoded by genetic association treats??
  8. 8. Example program (feel free to follow or adapt to your interest) • Example • Input = a disease (A) • Output = a ranked list of drugs (C) that might be used for treatment • Render the results of your workflow as a cytoscape network that illustrates the reasoning behind the predictions • Implementation • Python • Use a SPARQL endpoint such as • + identify and use another endpoint (e.g. EBI, UniProt) • ++ access pubmed articles and MeSH indexing
  9. 9. Python setup • pip install RDFLib, SPARQLWrapper, pandas…. • Hopefully Jupyter already installed ? else install it • get notebook from andas.ipynb • go to directory where you put the notebook • run it with • >jupyter notebook • should be ready to run
  10. 10. the notebook • will run a basic search for disease-gene-drug connections in wikidata • will sort the results by the number of intervening genes • will export the data to a tab-delimited file you can view in Excel, text editor, or load into cytoscape • Your job: • Run it and extend it by one or more of: • adapting the query • changing the way the results are sorted • working with the output in cytoscape to produce an informative visualization
  11. 11. example output rendered in cytoscape
  12. 12. Other queries from Day 1 (slides 48-54) • Drugs that target a cancer and impact a specific biological process • • Drugs that target a new disease linked via biological pathway with shared genes to disease the drug is now used to treat •
  13. 13. Possible inputs for adaptations • Browse and examine to see what you might make use of • e.g. • Type of physical interaction between gene and drug • Gene ontology annotation (what evidence codes?) • Disease ontology hierarchy • Drug characteristics
  14. 14. Other possible knowledge sources • SPARQL • UniProt • EBI SPARQL • look for unique identifiers on genes and proteins that you can use to link wikidata content to their content • Text • use the NCBI the E-utils API to programmatically access pubmed articles and MeSH indexing • Can use to build co-occurrence networks of e.g. MeSH terms
  15. 15. Good luck! Ask questions!
  16. 16. ABC ranking algorithms • Out of all C, which are most strongly related to A? • Rank by N shared B concepts • c2: 4 • c4:3 • c1: 1 • c3: 1 • c5:1 • c6:1 • Next level: adjust to down-weight highly connected nodes A B C c1 c2 c3 c4 c5 c6
  17. 17. ABC ranking algorithms – advanced (require large networks to be useful) • Wren – Average Minimum Weight (AMW) (Wren) • • Linking Term Count with Average Minimum Weight (LTC-AMW) (Yetisgen-Yildiz and Pratt) • thodology_for_literature-based_discovery_systems • Predicate inter-dependence (Rastegar-Mojarad) • hPBAWN/A%20new%20method.pdf